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Genetic Algorithm Based Feature Reduction For Depth Estimation Of Image  

Shin, Sung-Sik (Division of Computer Science and Engineering, Chonbuk National University)
Gwun, Ou-Bong (Division of Computer Science and Engineering, Chonbuk National University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea CI / v.48, no.2, 2011 , pp. 47-54 More about this Journal
Abstract
This paper describes the method to reduce the time-cost for depth estimation of an image by learning, on the basis of the Genetic Algorithm, the image's features. The depth information is estimated from the relationship among features such as the energy value of an image and the gradient of the texture etc. The estimation-time increases due to the large dimension of an image's features used in the estimating process. And the use of the features without consideration of their importance can have an adverse effect on the performance. So, it is necessary to reduce the dimension of an image's features based on the significance of each feature. Evaluation of the method proposed in this paper using benchmark data provided by Stanford University found that the time-cost for feature extraction and depth estimation improved by 60% and the accuracy was increased by 0.4% on average and up to 2.5%.
Keywords
Depth Estimation; Feature Reduction; Genetic Algorithm; Learning;
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Times Cited By KSCI : 1  (Citation Analysis)
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